Search Results for author:
Found 25 papers, 13 papers with code
RoNet: Rotation-oriented Continuous Image Translation
The generation of smooth and continuous images between domains has recently drawn much attention in image-to-image (I2I) translation.
Reliable long timescale decision-directed channel estimation for OFDM system
Decision-directed channel estimation (DDCE) is one kind of blind channel estimation method that tracks the channel blindly by an iterative algorithm without relying on the pilots, which can increase the utilization of wireless resource.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
ParsNets: A Parsimonious Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning
Then, to facilitate the generalization of local linearities, we construct a maximal margin geometry on the learned features by enforcing low-rank constraints on intra-class samples and high-rank constraints on inter-class samples, resulting in orthogonal subspaces for different classes and each subspace lies on a compact manifold.
Joint Design of Coding and Modulation for Digital Over-the-Air Computation
Due to its high communication efficiency, over-the-air computation (AirComp) has been expected to carry out various computing tasks in the next-generation wireless networks.
An Interdisciplinary Outlook on Large Language Models for Scientific Research
In this paper, we describe the capabilities and constraints of Large Language Models (LLMs) within disparate academic disciplines, aiming to delineate their strengths and limitations with precision.
The Many Voices of Duying: Revisiting the Disputed Essays Between Lu Xun and Zhou Zuoren
Moreover, 'People of Yue, Forget Not Your Ancestors' Instructions' seems to be either predominantly authored or extensively revised by Lu Xun given its notable stylistic similarities to 'Looking at the Land of Yue,' a piece Zhou Zuoren recognized as his own, but edited by Lu Xun.
AirCon: Over-the-Air Consensus for Wireless Blockchain Networks
Blockchain has been deemed as a promising solution for providing security and privacy protection in the next-generation wireless networks.
A New Approach to Extract Fetal Electrocardiogram Using Affine Combination of Adaptive Filters
Compared with the state-of-the-art MSF-ANC method, CRLS shows improved performance.
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
This paper presents an empirical study to build relation extraction systems in low-resource settings.
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information.
A Survey on Gradient Inversion: Attacks, Defenses and Future Directions
In particular, we dig out some critical ingredients from the iteration-based attacks, including data initialization, model training and gradient matching.
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with missing triples.
Ranked #49 on
Link Prediction
on FB15k-237
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they typically struggle to reason rare or emerging unseen entities.
Ranked #1 on
Link Prediction
on FB15k-237-ind
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
Artistic Style Discovery With Independent Components
Style transfer has been well studied in recent years with excellent performance processed.
Federated Unlearning via Class-Discriminative Pruning
Evaluated on CIFAR10 dataset, our method accelerates the speed of unlearning by 8. 9x for the ResNet model, and 7. 9x for the VGG model under no degradation in accuracy, compared to retraining from scratch.
Document-level Relation Extraction as Semantic Segmentation
Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples.
Ranked #4 on
Relation Extraction
on GDA
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt).
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1 (v1) metric)
Disentangled Contrastive Learning for Learning Robust Textual Representations
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks.
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM).
Ranked #1 on
Reading Comprehension
on ReCAM
(using extra training data)
Towards Robust Textual Representations with Disentangled Contrastive Learning
Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs.
Geometric Semantic Genetic Programming Algorithm and Slump Prediction
Research on the performance of recycled concrete as building material in the current world is an important subject.
Slope Stability Analysis with Geometric Semantic Genetic Programming
Furthermore, a model for slope stability analysis is established on the basis of geometric semantics.
